Impulsive Disturbances Research Articles

Instantaneous Frequency and Chirp Rate Estimation for Noisy Quadratic FM Signals by CNN

Deep learning and machine learning are widely employed in various domains. In this paper, Artificial Neural Network (ANN) and Convolution Neural Network (CNN) are used to estimate the Instantons Frequency (IF), Linear Chirp Rate (LCR), and Quadratic Chirp Rate (QCR) for Quadratic Frequency Modulated (QFM) signals under Additive White Gaussian (AWG) noise and Additive Symmetric alpha Stable (ASαS) noise. SαS distributions are impulsive noise disturbances except for a few circumstances, lack a closed-form Probability Density Function (PDF), and an infinite second-order statistic. Geometric SNR (GSNR) is used to determine the impulsiveness of mixture noise for Gaussian and SαS noise. ANN is a machine learning classifier with few layers that reduce FE, LCRE, and QCRE complexity and achieve high accuracy. CNN is a deep learning classifier that is built with multiple layers of FE, LCRE, and QCRE. CNN is more accurate than ANN when dealing with large amounts of data and determining optimal features. The results reveal that SαS noise is substantially more damaging to FE, LCRE, and QCRE than Gaussian noise, even when the magnitude is modest, and it is less damaging when alpha is greater than one. After training DCNN for FE, LCRE, and QCRE estimation of QFM signals. The 2D-CNN model accuracy achieved 98.7603 and 1D-CNN is 75.8678 for ten epochs. ANN model accuracy achieved 37.5 for 1000 epochs. The accuracy of TFD (spectrogram & pspectrum) for frequency estimation of QFM signals was 38.4254 by spectrogram and 38.6746 by pspectrum.

A combined VOF-RANS approach for studying the evolution of incipient two-dimensional wind-driven waves over a viscous liquid

Recent laboratory experiments have revealed that important insights into the physical processes involved in the wind-driven generation of surface waves may be obtained by varying the viscosity of the carrying liquid over several orders of magnitude. The present paper reports on the development of a companion approach aimed at studying similar phenomena through numerical simulation, a way expected to remove some of the experimental limitations, especially in the near-interface region, and to allow the relative influence of several physical processes to be assessed by disregarding or inactivating arbitrarily some of them. After reviewing available options, we select and approach based on the combination of a volume of fluid technique to track the evolution of the air–liquid interface, and a two-dimensional Reynolds-averaged version of the Navier–Stokes equations supplemented with a turbulence model to predict the velocity and pressure fields in both fluids. We examine the formal and physical frameworks in which such a time-dependent two-dimensional formulation is meaningful, and close the governing momentum equations with the one-equation Spalart–Allmaras model which directly solves a transport equation for the eddy viscosity. For this purpose, we assume the interface to behave as a rigid wall with respect to turbulent fluctuations in the air, and implement a versatile algorithm to compute the local distance to the interface whatever its shape. We first assess the performance of this model in unseparated and separated single-phase flows over a wavy rigid wall, which are of specific relevance with respect to wind-wave generation. Then, we discuss the initialization protocol used in two-phase simulations, which involves an impulse disturbance with a white noise distribution applied to the interface position. We finally present some examples of interface evolutions obtained at several wind speeds with liquids of various viscosities, and discuss the underlying physics revealed by the associated statistics of interface disturbances, streamline patterns and energy spectra.

A Lorentzian Adaptive Filter-Based Mechanism for Resilience Against Impulsive Disturbances Applied to a Grid-Tied Solar PV System

This paper demonstrates a solar photovoltaic (PV) system equipped with a Lorentzian adaptive filter (LAF) based mechanism, whose prominent capability is its ability to filter impulsive disturbances in the acquired sensor output. The primary motivation of this work is to achieve resiliency of load weight estimation against the adverse effects of impulsive disturbances in noisy environments, which present a unique difficulty for most filtering mechanisms. The swift and accurate information of the load weight assists the system to achieve an accelerated response at dynamic conditions. Moreover, on careful selection of the LAF gains using the procedure presented in this work, the effective resilience against impulsive disturbances is achieved without compromising the system response time. The detailed examination of the real time intermediate signals is presented to explain the filter functioning and abilities. The LAF equipped PV system behavior is compared experimentally against multiple other filters, to examine its improved behavior. The study considers the local loads to be non-ideal. The presented system inhibits the adverse effects of such loads from affecting the grid, via its power quality improvement actions. The system control also makes it robust against the grid voltage anomalies.

Aperiodic intermittent control for exponential input-to-state stabilization of nonlinear impulsive systems

This article addresses the exponential input-to-state stabilization (EISS) problem for a class of nonlinear impulsive systems with external inputs, in which both continuous external inputs and impulsive disturbance are fully considered. The aperiodic intermittent control (APIC) strategy is designed by limiting the proportion of the control width in the control process, some sufficient conditions for the system to realize EISS are given, and the range of admissible impulse time sequences is derived. The designed APIC can overcome the dual adverse effects caused by continuous external input and admissible impulse time sequences, and help impulsive systems to obtain exponential input-state stability. Finally, the validity of the theorems is verified by numerical simulations.

Synchronization of neural networks involving unmeasurable states and impulsive disturbances by observer and feedback control

In this article, the problem of synchronization for neural networks is studied by way of the feedback control based on the state estimation even thought the existence of delayed impulsive disturbances which produce negative impacts on the control. Meanwhile, two cases are fully addressed, where drive neural networks and response neural networks involve unmeasurable states respectively. The key matters, which we settle, are the construction of the observer and the design of feedback control based on the state estimation, in the presence of delayed impulsive disturbances and unmeasurable states. By utilizing the linear matrix inequality (LMI) and the delayed impulsive differential inequality, certain adequate qualifications are gotten to ensure synchronization. Moreover, note that our proposed conditions not only drop the limitation of impulse intervals and impulse sizes, but also realize the coexistence of delayed impulses and macroscale impulses. Finally, expository examples and their numerical modeling are appeared to prove the availability of main results.

Recursive identification of the time-varying frequency parameters of a space solar power satellite using a robust subspace tracking method based on the variable forgetting factor

Considering changes in the structural configuration caused by the rotation of solar panel arrays in space solar power satellites, this study proposes an improved robust recursive subspace tracking algorithm to identify the time-varying modal frequency parameters of the system. In this algorithm, variable forgetting factor and robust weight coefficient are involved to improve the tracking adaptability of time-varying system and decrease the influence of impulsive disturbance. Then, the time-varying pseudo modal frequency parameters are identified using the recursive projection subspace tracking. In the numerical simulations, the influence of the different rotation speeds of the solar panel arrays on the frequency identification results is studied. The computational results for the different rotation speeds reveal that the tracking performance of the proposed algorithm is better than that of the original method for time-varying system. Under the influence of impulsive disturbances, the relative values of the average relative error under the proposed algorithm are approximately 65.9–75.2% less than those under the original algorithm. Additionally, the results reveal that when the rotation speed of the solar panel arrays is time-varying, the proposed algorithm achieves a better tracking performance than the conventional recursive methods by the adjustment of the variable forgetting factor. When the signal-to-noise ratio is 15 dB, the absolute and relative values of the average relative error under the proposed algorithm are less by approximately 6.4% and 64.8%, respectively, under the two reference methods with a fixed forgetting factor.

Finite-time synchronization of discontinuous fuzzy neural networks with mixed time-varying delays and impulsive disturbances

In this paper, the finite-time synchronization issue is addressed for discontinuous fuzzy neural networks with mixed time-varying delays and impulsive disturbances. The dynamic impulsive gain about the impulsive moment is proposed as an alternative to the previous static impulsive gain. The time-varying impulsive gain can combine the synchronizing and desynchronizing impulses to directly observe their effects on the system, avoiding the classification discussion. In order to study the finite-time synchronization of the drive–response system, we obtain an equivalent system from the original fuzzy neural networks based on the concepts of Filippov solutions and differential inclusion theory. Furthermore, by constructing new Lyapunov functions that combine the 1-norm and the comparative system method, some algebraic sufficient conditions for the synchronization of the drive system and the response system in finite time are established, which are based on two types of novel non-chattering controllers. In addition, the parameters of the system itself can simply demonstrate the sufficient conditions that have been obtained her e, avoiding the time-consuming calculation of matrix inequalities. Moreover, the settling time estimation scheme have been established. Eventually, numerical examples are provided to demonstrate the effectiveness of the control scheme.

Fractional-Order Impulsive Delayed Reaction-Diffusion Gene Regulatory Networks: Almost Periodic Solutions

The paper is oriented on the existence of almost periodic solutions of factional-order impulsive delayed reaction-diffusion gene regulatory networks. Caputo type fractional-order derivatives and impulsive disturbances at not fixed instants of time are considered. New almost periodic and perfect Mittag–Leffler stability criteria are proposed. Lyapunov’s like impulsive functions, the properties of the fractional derivatives and comparison principle are the main tools in the investigation. Illustrative examples are also presented to demonstrate the proposed criteria. Our results contribute to the development of qualitative the theory of fractional-order gene regulatory networks.

Global Stabilization of Delayed Feedback Financial System Involved in Advertisement under Impulsive Disturbance

Diffusion is an inevitable important factor in advertising dynamic systems. However, previous literature did not involve this important diffusion factor, and only involved the local stability of the advertising model. This paper develops a global stability criterion for the impulsive advertising dynamic model with a feedback term under the influence of diffusion. Since global stability requires the unique existence of equilibrium points, variational methods are employed to solve it in the infinite dimensional function space, and then a global stability criterion of the system is derived by way of the impulse inequality lemma and orthogonal decomposition of a class of Sobolev spaces. Numerical simulations verify the effectiveness of the proposed method.

Effect of large delay on pinning impulsive synchronization control: A strict comparison principle

SummaryOver the past decades, various successful results have shown the discoveries and progress of impulsive synchronization. Yet, little attention has been devoted to the effects of the impulsive elasticity coefficient on network synchronization for both impulsive control problems and impulsive disturbance problems. In this work, we study impulsive pinning synchronization for coupled neural networks from a large delay perspective. Some flexible synchronization criteria are derived based on the average impulsive interval method. It has shown that synchronization of coupled neural networks with arbitrarily finite delay can be achieved via controlling a small fraction of nodes. Specifically, by applying a strict comparison principle for impulsive delayed neural networks, the effects of the impulsive elasticity coefficient on synchronization are revealed to be dissimilar from the case of delay‐free neural networks. One can verify that increasing the elasticity coefficient may bring a desynchronizing impact on the original synchronized networks. More ulteriorly, the generated criteria will still hold if the delay is sufficiently large, which implies that the delay is independent of the size of the impulsive interval. Our work is an essential step toward investigating the role of the elasticity coefficient on synchronization. Furthermore, the synchronization region of different impulsive weights, impulsive intervals, and impulsive control proportions are described thoroughly.

Fixed-Time and Predefined-Time Stability of Impulsive Systems

Dear Editor, This letter establishes several criteria for fixed-time stability and predefined-time stability of impulsive systems. First, sufficient conditions for fixed-time stability of impulsive systems are presented to treat the destabilizing impulses and hybrid impulses involving multiple jump maps by fixed-time control without linear feedback regulation. It determines the robustness of nonlinear systems against impulsive disturbance which has destabilizing and hybrid effect to dynamics. Then, the predefined-time stability of impulsive systems is ensured under the requirement that the hybrid impulses have stabilizing accumulative effect. Finally, the validity of theoretical results is verified by the allocation of mobile agents on a segment via fixed-time control with impulsive regulation.

Methodology of investigation the dynamics of longitudinally moving systems under the action of impulse perturbations

A methodology for researching oscillations of one-dimensional elastic systems under the influence of impulse disturbances has been developed. The mathematical model of the considered process is boundary value problems for hyperbolic equations of the second order with an irregular right-hand side and a mixed derivative with respect to linear and time variables. As for the irregularity of the right part, it takes into account the impulse effect on the research object and has the most important theoretical and practical periodic character; and with the help of the mixed derivative in the Euler variables, the longitudinal movement of the studied systems is partially taken into account. Using the basic ideas of asymptotic integration of equations with partial derivatives, the basic propositions of the wave theory of motion adapted to the considered class of problems, the principle of single-frequency oscillations in nonlinear systems with concentrated masses and distributed parameters, the method of regularization of impulse disturbances, equations in the standard form were obtained that describe the determining parameters of nonlinear oscillations for resonant and non-resonant cases. It is shown: that the longitudinal movement of the systems has a significant effect on the natural frequency of oscillations, and therefore on the period of impulse disturbances during which the resonance process takes place; the amplitude of the passage of resonance depends significantly on the speed of movement. The results obtained in the work can serve as a basis for choosing the operating modes of some classes of systems characterized by longitudinal movement in order to avoid resonant processes in them.

Finite-time average consensus of directed second-order multi-agent systems with Markovian switching topology and impulsive disturbance

Finite-time average consensus of directed second-order multi-agent systems with Markovian switching topology and impulsive disturbance

Input-to-State Stability of Nonlinear Impulsive Systems Subjects to Actuator Saturation and External Disturbance.

This article mainly explores the local input-to-state stability (LISS) property of a class of nonlinear systems via a saturated control strategy, where both the external disturbance and impulsive disturbance being fully considered. In terms of the Lyapunov method and inequality techniques, some sufficient conditions under which the system can be made LISS are proposed, and the elastic constraint relationship among saturated control gain, rate coefficients, external disturbance, and domain of initial value is revealed. Moreover, the optimization design procedures are provided with the hope of obtaining the estimates of admissible external disturbance and domain of initial value as large as possible, where the corresponding saturated control law can be designed by solving LMI -based conditions. In the absence of an external disturbance, the locally exponential stability (LES) property can also be presented with a set of more relaxed conditions. Finally, two examples are presented to reveal the validity of the obtained results.

The Lateral and Directional Stability Behavior for Two Aircraft Models

The current study looks at flight behavior in lateral and directional motion.The investigation involves setting up the aircraft under an equilibrium condition with a small disturbance applied to it. Plotting the side slip angle, roll angle, and yaw angle with respect to time in the presence of disturbances can be used to investigate flight behavior in lateral and directional motion. Here the disturbance can be simulated by the movement of the aileron or rudder, in which these two control surfaces can be designed to move in a single impulse or multiple impulse disturbance mode. These two disturbance modes are used on the Beechcraft 99 and Cessna T37 aircraft. Both impulse disturbance models are used for the aileron and the rudder. However, in the current work, the Beechcraft 99 receives a single impulse, whereas the Cessna T37 receives multiple impulses. The implementation of such disturbances found that the Beechcraft 99 represented the aircraft that would be able to go back to its initial condition in response to a single impulse disturbance modewhile the Cessna T37 aircraft requires a little more time to return to its original yaw angle. Following the implementation of these twotypes of disturbances, it was discovered that each aircraft has the ability to return to its initial condition, but at varying times to reach its steady state solution.

Instantaneous Frequency Estimation of FM Signals under Gaussian and Symmetric α-Stable Noise: Deep Learning versus Time–Frequency Analysis

Deep learning (DL) and machine learning (ML) are widely used in many fields but rarely used in the frequency estimation (FE) and slope estimation (SE) of signals. Frequency and slope estimation for frequency-modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sound navigation and ranging (SONAR), and radio detection and ranging (RADAR) measurements. This work proposed a novel frequency estimation technique for instantaneous linear FM (LFM) sinusoidal wave using deep learning. Deep neural networks (DNN) and convolutional neural networks (CNN) are classes of artificial neural networks (ANNs) used for the frequency and slope estimation for LFM signals under additive white Gaussian noise (AWGN) and additive symmetric alpha stable noise (SαSN). DNN is composed of input, output, and two hidden layers, where several nodes in the first and second hidden layers are 25 and 8, respectively. CNN is the content input layer; many hidden layers include convolution, batch normalization, ReLU, max pooling, fully connected, and dropout. The output layer consists of a fully connected softmax and classification layers. SαS distributions are impulsive noise disturbances found in many communication environments such as marine systems, their distribution lacks a closed-form probability density function (PDF), except for specific cases, and infinite second-order statistics, hence geometric SNR (GSNR) is used in this work to determine the effect of noise in a mixture of Gaussian and SαS noise processes. DNN is a machine learning classifier with few layers for reducing FE and SE complexity. CNN is a deep learning classifier, designed with many layers, and proved to be more accurate than DNN when dealing with big data and finding optimal features. Simulation results show that SαS noise can be much more harmful to the FE and SE of FM signals than Gaussian noise. DL and ML can significantly reduce FE complexity, memory cost, and power consumption as compared to the classical FE based on time–frequency analysis, which are important requirements for many systems, such as some Internet of Things (IoT) sensor applications. After training CNN for frequency and slope estimation of LFM signals, the performance of CNN (in terms of accuracy) can give good results at very low signal-to-noise ratios where time–frequency distribution (TFD) fails, giving more than 20 dB difference in the GSNR working range as compared to the classical spectrogram-based estimation, and over 15 dB difference with Viterbi-based estimate.

Finite-time stabilization for uncertain nonlinear systems with impulsive disturbance via aperiodic intermittent control

This paper ensures finite-time stabilization (FTS) and finite-time contractive stabilization (FTCS) for impulsive systems with time-varying parametric uncertainties by investigating the design of an aperiodic intermittent controller, where uncertain impulsive disturbance is generated at the instants caused by the control action to the plant. Based on the Lyapunov methods, several sufficient conditions are proposed to guarantee the FTS and FTCS. In the framework of finite-time, two relationships between impulsive disturbance, aperiodic control periods, and aperiodic control widths are established to guarantee the FTS and FTCS for the system, respectively. Moreover, such two relationships can potentially increase the freedom of the designed control periods and control widths. Finally, two numerical simulations and a practical application are given to show the validity of the aperiodic intermittent controller.

Pinning synchronization for markovian jump neural networks with uncertain impulsive effects

This work concentrates on synchronization of neural networks (NNs) with Markovian parameters, where the Markov chain has partially unknown transition probabilities (PUTP). Due to the existence of interference and noise in practice, we combine the uncertain variable with the complex coupling term as the impulsive disturbance of NNs. A corresponding mode-dependent pinning controller is designed to reduce the control costs, and synchronization error system is also derived, whose impulsive update state is listed separately. A sufficient condition of synchronization for NNs is completed by constructing a Lyapunov functional candidate and a series of iterations. Because the disturbance should avoid being too frequent to guarantee synchronization of NNs, the allowed minimum interval h of the impulsive disturbance is derived. Finally, the correctness and the superiority of the developed result are illustrated by a numerical example.

Nonsingular Terminal Sliding-Mode Control of Second-Order Systems Subject to Hybrid Disturbances

This brief proposes a novel terminal sliding-mode (TSM) control design for second-order systems subject to both continuous and impulsive disturbances. The problem of singularity is effectively eliminated via the proposed approach. To ensure existence of the sliding mode in the presence of hybrid disturbances, necessary constraints on disturbances are given. It shows that the system can reach the sliding surface within finite time, and then converge to the origin within finite time under the proposed TSM control, which achieves finite-time stabilization of second-order systems. Finally, numerical examples are given to validate the obtained results.

Mean square stability of discrete-time linear systems with random impulsive disturbances

Mean square stability of discrete-time linear systems with random impulsive disturbances